Normalized_Stock_Prices_and_Random_Walk.ipynb

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      "execution_count": null,
      "source": "!pip install -q yfinance\nimport yfinance as yf\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom datetime import datetime, timedelta",
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      "source": "# Define the ticker symbols\ntickers = ['AAPL', 'MSFT', 'GOOGL', 'AMZN', 'FB', 'TSLA', 'NVDA', 'JPM', 'V', 'JNJ']\n# Define the start and end dates\nend_date = datetime.now()\nstart_date = end_date - timedelta(days=5*365)\n# Download the data\ndata = yf.download(tickers, start=start_date, end=end_date)['Adj Close']\n# Normalize the data\nnormalized_data = data / data.iloc[0]\n# Plot the normalized data\nplt.figure(figsize=(14, 7))\nfor i in normalized_data.columns.values:\n    plt.plot(normalized_data.index, normalized_data[i], lw=2, label=i)\nplt.legend(loc='upper left', fontsize=12)\nplt.ylabel('Normalized price')\nplt.title('Normalized prices of 10 US stocks over the last 5 years')\nplt.grid(True)\nplt.show()",
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      "source": "from matplotlib.lines import Line2D\n# Define colors for the plots\ncolors = plt.rcParams['axes.prop_cycle'].by_key()['color']\n# Plot the real cumulative returns\nplt.figure(figsize=(14, 7))\nfor i, ticker in enumerate(real_returns.columns):\n    plt.plot(real_returns.index, real_returns[ticker], lw=2, label=ticker, color=colors[i % len(colors)])\nplt.ylabel('Cumulative returns')\nplt.title('Real Cumulative Returns over the last 5 years')\nplt.legend()\nplt.grid(True)\nplt.show()\n# Plot the simulations\nplt.figure(figsize=(14, 7))\nfor t, ticker in enumerate(normalized_data.columns):\n    for i in range(num_simulations):\n        plt.plot(normalized_data.index, simulated_returns_array[i, :, t], lw=2, alpha=0.4, color=colors[t % len(colors)])\nplt.ylabel('Cumulative returns')\nplt.title('Monte Carlo Random Walk Simulations of Cumulative Returns over the last 5 years')\nhandles = [Line2D([0], [0], color=colors[i % len(colors)], lw=2) for i in range(len(normalized_data.columns))]\nplt.legend(handles, normalized_data.columns)\nplt.grid(True)\nplt.show()",
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      "source": "# Normalize the simulated returns\nsimulated_returns_random_normalized = simulated_returns_random.groupby(level='Simulation').apply(lambda x: x - x.iloc[0])\n# Plot the normalized simulations\nplt.figure(figsize=(14, 7))\nfor t, ticker in enumerate(normalized_data.columns):\n    for i in range(num_simulations):\n        plt.plot(normalized_data.index, simulated_returns_random_normalized.loc[i, ticker], lw=2, alpha=0.4, color=colors[t % len(colors)])\nplt.ylabel('Cumulative returns')\nplt.title('Normalized Random Walk Simulations of Cumulative Returns over the last 5 years (No Trend)')\nhandles = [Line2D([0], [0], color=colors[i % len(colors)], lw=2) for i in range(len(normalized_data.columns))]\nplt.legend(handles, normalized_data.columns)\nplt.grid(True)\nplt.show()",
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